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The number of control actuators available on spark-ignition engines is rapidly increasing to meet demand for improved fuel economy and reduced exhaust emissions. The added complexity greatly complicates control strategy development because there can be a wide range of potential actuator settings at each engine operating condition, and map-based actuator calibration becomes challenging as the number of control degrees of freedom expand significantly. Many engine actuators, such as variable valve actuation and flow control valves, directly influence in-cylinder combustion through changes in gas exchange, mixture preparation, and charge motion. The addition of these types of actuators makes it difficult to predict the influences of individual actuator positioning on in-cylinder combustion without substantial experimental complexity.

This paper presents the transient power optimization of an organic Rankine cycle waste heat recovery (ORC-WHR) system operating on a heavy-duty diesel (HDD). The optimization process is carried on an experimentally validated, physics-based, high fidelity ORC-WHR model, which consists of parallel tail pipe and EGR evaporators, a high pressure working fluid pump, a turbine expander, etc. Three different ORC-WHR mixed vapor temperature (MVT) operational strategies are evaluated to optimize the ORC system net power: (i) constant MVT; (ii) constant superheat temperature; (iii) fuzzy logic superheat temperature based on waste power level. Transient engine conditions are considered in the optimization. Optimization results reveal that adaptation of the vapor temperature setpoint based on evaporation pressure strategy (ii) provides 1.1% mean net power (MNP) improvement relative to a fixed setpoint strategy (i).

Two methods for analyzing and evaluating the environmental control system on an advanced aircraft as described in this paper include the conventional first law energy conservation technique and the second law entropy generation minimization technique. Simplified analytical models of the ECS are developed for each method and compared to determine the validity of using the latter to facilitate the design process in optimizing the overall system for a minimum gross takeoff weight (GTW). Preliminary results have illustrated the importance of taking into account system optimization based on system (or component) efficiency. For instance, even though different values were obtained for the rate of entropy generation, the second law analysis of a shell-in-tube heat exchanger led to an optimum tube diameter of 0.12 in (3.05 mm) when both R-12 and R-114 were used as the refrigerant in the vapor cycle.

A nonlinear eight degree of freedom vehicle model has been used to examine the effects of roll stiffness on handling and performance. In addition, various control strategies have been devised which vary the total roll couple distribution in order to improve cornering capability and stopping distance. Of all cases tested, a controller which varies the total roll stiffness based on roll angle feedback, and continuously updates the roll couple distribution as a function of steering wheel angle, braking input, and the total roll stiffness, yields the greatest improvements in collision avoidance.

Predictable handling of a racecar may be achieved by tailoring chassis stiffness so that roll stiffness between sprung and unsprung masses are due almost entirely to the suspension. In this work, the effects of overall chassis flexibility on roll stiffness and wheel camber response, will be determined using a finite element model (FEM) of a Winston Cup racecar chassis and suspension. The FEM of the chassis/suspension is built from an assembly of beam and shell elements using geometry measured from a typical Winston cup race configuration. Care has been taken to model internal constraints between degrees-of-freedom (DOF) at suspension to chassis connections, e.g. t ball and pin joints and internal releases. To validate the model, the change in wheel loads due to an applied jacking force that rolls the chassis agrees closely with measured data.

Autonomous driving technologies can provide better safety, comfort and efficiency for future transportation systems. Most research in this area has mainly been focused on developing sensing and control approaches to achieve various autonomous driving functions. Very little of this research, however, has studied how to efficiently handle sensing exceptions. A simple exception measured by any of the sensors may lead to failures in autonomous driving functions. The autonomous vehicles are then supposed to be sent back to manufacturers for repair, which takes both time and money. This paper introduces an efficient approach to make human drivers able to online teach autonomous vehicles to drive under sensing exceptions. A human-vehicle teaching-and-learning framework for autonomous driving is proposed and the human teaching and vehicle learning processes for handling sensing exceptions in autonomous vehicles are designed in detail.

Rollover prevention is now part of complete vehicle stability control systems for many vehicles. Given that rollover is predominantly associated with vehicles with high centers of gravity, the targeted vehicles for rollover protection include medium and heavy duty commercial vehicles. Unfortunately, the chassis designs of these vehicles are often so compliant in torsion that the ends of the vehicles may have significantly different roll responses at any given time. The potential need to assess and correct for the roll behavior of the front and rear ends of the vehicle is the subject of this paper. Most rollover mitigation research to date has used rigid chassis assumptions in modeling the vehicle. This paper deals with the roll control of vehicles with torsionally flexible chassis based on a yaw-correction system.

Refinements were made to a post-processing technique, termed the Thermal Stratification Analysis (TSA), that couples the mass fraction burned data to ignition timing predictions from the autoignition integral to calculate an apparent temperature distribution from an experimental HCCI data point. Specifically, the analysis is expanded to include all of the mass in the cylinder by fitting the unburned mass with an exponential function, characteristic of the wall-affected region. The analysis-derived temperature distributions are then validated in two ways. First, the output data from CFD simulations are processed with the Thermal Stratification Analysis and the calculated temperature distributions are compared to the known CFD distributions.

This paper presents a new way to evaluate vehicle speed profile aggressiveness, quantify it from the perspective of the rapid speed fluctuations, and assess its impact on vehicle fuel economy. The speed fluctuation can be divided into two portions: the large-scale low frequency speed trace which follows the ongoing traffic and road characteristics, and the small-scale rapid speed fluctuations normally related to the driver's experience, style and ability to anticipate future events. The latter represent to some extent the driver aggressiveness and it is well known to affect the vehicle energy consumption and component duty cycles. Therefore, the rapid speed fluctuations are the focus of this paper. Driving data collected with the GPS devices are widely adopted for study of real-world fuel economy, or the impact on electrified vehicle range and component duty cycles.

In developing a nonlinear steering system model for vehicle simulation, it was determined that proper inclusion of system friction is necessary to correctly predict steering wheel torque response in on-center driving using simulation models. A method to characterize the inherent friction behavior for a given steering gear has been developed and performed on two types of power steering gears: a recirculating ball gear and a rack-and-pinion gear. During this research it was discovered that levels of static and dynamic friction can differ widely for these two types. Therefore this characterization procedure provides a method to ascertain both static and dynamic friction levels. The results from these tests show that friction levels can depend on steering gear input shaft position, steering gear input angular velocity and steering gear loading conditions.

Design of military vehicle needs to meet often conflicting requirements such as high mobility, excellent fuel efficiency and survivability, with acceptable cost. In order to reduce the development cost, time and associated risk, as many of the design questions as possible need to be addressed with advanced simulation tools. This paper describes a methodology to design a fuel efficient powerpack unit for a series hybrid electric military vehicle, with emphasis on the e-machine design. The proposed methodology builds on previously published Finite element based analysis to capture basic design features of the generator with three variables, and couples it with a model reduction technique to rapidly re-design the generator with desired fidelity. The generator is mated to an off the shelf engine to form a powerpack, which is subsequently evaluated over a representative military drive cycles.

Military vehicles experience a wide range of duty cycles depending on the place and purpose of their deployment. Vehicle fuel consumption directly depends on those use cases, which are ranging from patrolling during peace keeping operations to direct engagements in hostiles areas. Vehicle design should accommodate this wide range of operation modes to maximize the vehicle practicality during their service life. This paper aims to quantify the sensitivity of the powerpack design for a notional 15-ton series hybrid electric vehicle for two highly dynamic military drive cycles. The optimal design for a powerpack (engine coupled with a generator) will be separately determined for each of the use cases through a previously developed optimization routine that use the Genetic Algorithm. For each iteration of the Genetic Algorithm a design benchmarking was incorporated by using Dynamic Programming.

This paper presents an Organic Rankine Cycle (ORC) system model for heavy-duty diesel (HDD) applications. The dynamic, physics-based model includes: heat exchangers for parallel exhaust and EGR circuits, compressible vapor working fluid, distribution and flow control valves, a high pressure pump, and a reservoir. A finite volume method is used to model the evaporator, and a pressure drop model is included to improve the accuracy of predictions. Experimental results obtained on a prototype ORC system are used for model calibration and validation. Comparison of predicted and measured values under steady-state conditions is pursued first, followed by the analysis of selected transient events. Validation reveals the model’s ability to track real-world temperature and pressure dynamics of the ORC system. Therefore, this modeling framework is suitable for future system design studies, optimization of ORC power generation, and as a basis for development of control-oriented ORC models.

This paper investigates the impact of battery cooling ancillary losses on fuel economy, and optimal control strategy for a series hybrid electric truck with consideration of cooling losses. Battery thermal model and its refrigeration-based cooling system are integrated into vehicle model, and the parasitic power consumption from cooling auxiliaries is considered in power management problem. Two supervisory control strategies are compared. First, a rule-based control strategy is coupled with a thermal management strategy; it controls power system and cooling system separately. The second is optimal control strategy developed using Dynamic Programming; it optimizes power flow with consideration of both propulsion and cooling requirement. The result shows that battery cooling consumption could cause fuel economy loss as high as 5%.

This paper develops a methodology to optimize the supervisory controller for a heavy-duty series hybrid electric vehicle, with consideration of battery aging and cooling loss. Electrochemistrybased battery aging model is integrated into vehicle model. The side reaction, reductive electrolyte decomposition, is modeled to determine battery aging rate, and the thermal effect on this reaction rate is considered by Arrhenius Law. The resulting capacity and power fading is included in the system-level study. Sensitivity analysis shows that battery aging could cause fuel economy loss by 5.9%, and increasing temperature could improve fuel economy at any given state-of-health, while accelerating battery aging. Stochastic dynamic programming algorithm is applied to a modeled system to handle the tradeoff between two objectives: maximizing fuel economy and minimizing battery aging.

Commercial vehicles transport the majority of the inland freight in US and a significant number of passengers. They are large fuel consumers as they operate a large number of hours per day, pulling heavy loads. The increasing fuel price and the Green House Gas emission regulation have provided a strong impetus for new technologies capable of improving the commercial vehicle fuel economy. Among others, optimized powertrain control can improve the vehicle fuel economy, particularly if it is based on accurate information about the instantaneous load demand. Furthermore, model-based online vehicle parameter estimator is critical for implementation of an adaptive vehicle controller. While vehicle mass estimation has been successfully demonstrated, rolling resistance and aerodynamic drag estimation has not been fully explored yet. This paper examines this problem using model-based approach with a supervisory data extraction scheme.

Due to titanium's excellent strength-to-weight ratio and high corrosion resistance, titanium and its alloys have great potential to reduce energy usage in vehicles through a reduction in vehicle mass. The mass of a road vehicle is directly related to its energy consumption through inertial requirements and tire rolling resistance losses. However, when considering the manufacture of titanium automotive components, the machinability is poor, thus increasing processing cost through a trade-off between extended cycle time (labor cost) or increased tool wear (tooling cost). This fact has classified titanium as a “difficult-to-machine” material and consequently, titanium has been traditionally used for application areas having a comparatively higher end product cost such as in aerospace applications, the automotive racing segment, etc., as opposed to the consumer automotive segment.

As engines are equipped with an increased number of control actuators to meet fuel economy targets, they become more difficult to control and calibrate. The additional complexity created by a larger number of control actuators motivates the use of physics-based control strategies to reduce calibration time and complexity. Combustion phasing, as one of the most important engine combustion metrics, has a significant influence on engine efficiency, emissions, vibration and durability. To realize physics-based engine combustion phasing control, an accurate prediction model is required. This research introduces physics-based control-oriented laminar flame speed and turbulence intensity models that can be used in a quasi-dimensional turbulent entrainment combustion model. The influence of laminar flame speed and turbulence intensity on predicted mass fraction burned (MFB) profile during combustion is analyzed.

The limited operational range of low temperature combustion engines is influenced by near-wall conditions. A major factor is the accumulation and burn-off of combustion chamber deposits. Previous studies have begun to characterize in-situ combustion chamber deposit thermal properties with the end goal of understanding, and subsequently replicating the beneficial effects of CCD on HCCI combustion. Combustion chamber deposit thermal diffusivity was found to differ depending on location within the chamber, with significant initial spatial variations, but a certain level of convergence as equilibrium CCD thickness is reached. A previous study speculatively attributed these spatially dependent CCD diffusivity differences to either local differences in morphology, or interactions with the fuel-air charge in the DI engine. In this work, the influence of directly injected gasoline on CCD thermal diffusivity is measured using the in-situ technique based on fast thermocouple signals.

The 48V mild hybrid technology is emerging as a very attractive option for high-volume vehicle electrification. Compared to high-voltage hybrids, the 48V system has a potential of achieving competitive fuel economy with significantly lower incremental costs. While previous studies of 48V mild hybrid systems discussed vehicle configuration, power management strategy and electric machine design, quantitative assessment of fuel economy under real-world conditions remains an open topic. Objectives of this paper are to propose a methodology for categorizing real-world cycles based on driver aggressiveness, and to subsequently analyze the impact of driving patterns on fuel saving potentials with a 48V mild hybrid system. Instead of using the certification test cycles to evaluate the fuel economy, real-world cycles are extracted from 2001-2003 Southern California Household Travel Survey.